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diff --git a/runtime/neurun/core/src/exec/interp/ExecManager.cc b/runtime/neurun/core/src/exec/interp/ExecManager.cc
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+++ b/runtime/neurun/core/src/exec/interp/ExecManager.cc
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+/*
+ * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ExecManager.h"
+#include "ExecEnv.h"
+#include "Interpreter.h"
+
+#include "util/logging.h"
+
+#include <cpp14/memory.h>
+
+namespace neurun
+{
+namespace exec
+{
+namespace interp
+{
+
+void ExecManager::execute(const IODescription &desc)
+{
+  /************************************************************************
+   * Prepare execution model (submodel)
+     It may execute divided model
+     but now consider model inference is done at interpreter
+   ***********************************************************************/
+  ir::OperandIndexMap<std::shared_ptr<ITensor>> tensor_map;
+
+  for (uint32_t n = 0; n < _graph.getInputs().size(); n++)
+  {
+    ir::IOIndex index{n};
+    const auto input_index = _graph.getInputs().at(index);
+    const auto &input = *desc.inputs.at(n);
+
+    auto input_tensor = std::make_shared<ROTensor>(input.info);
+    input_tensor->setData(std::make_shared<const ir::ExternalData>(
+        reinterpret_cast<const uint8_t *>(input.buffer), input.size));
+    tensor_map[input_index] = input_tensor;
+  }
+
+  for (uint32_t n = 0; n < _graph.getOutputs().size(); n++)
+  {
+    ir::IOIndex index{n};
+    const auto output_index = _graph.getOutputs().at(index);
+    const auto &output = *desc.outputs.at(n);
+
+    auto output_tensor = std::make_shared<Tensor>(output.info);
+    output_tensor->setBuffer(
+        std::make_shared<ExternalBuffer>(reinterpret_cast<uint8_t *>(output.buffer), output.size));
+    tensor_map[output_index] = output_tensor;
+  }
+
+  /************************************************************************
+   * Prepare execution environment
+     Execution environment will be assigned to invoked interpreter instance
+   ***********************************************************************/
+
+  std::unique_ptr<ExecEnv> interp_env = nnfw::cpp14::make_unique<ExecEnv>(_graph);
+
+  // Assign input tensor into interpreter execution environment
+  for (auto index : _graph.getInputs())
+  {
+    if (tensor_map.find(index) != tensor_map.end())
+    {
+      VERBOSE(INTERPRETER) << "Assign input tensor. operand index:" << index.value() << std::endl;
+      interp_env->assignTensor(index, tensor_map.at(index));
+    }
+  }
+
+  // Assign output tensor into interpreter execution environment
+  for (auto index : _graph.getOutputs())
+  {
+    if (tensor_map.find(index) != tensor_map.end())
+    {
+      VERBOSE(INTERPRETER) << "Assign output tensor. operand index: " << index.value() << std::endl;
+      interp_env->assignTensor(index, tensor_map.at(index));
+    }
+  }
+
+  // Allocate constant tensor
+  _graph.operands().iterate([&](const ir::OperandIndex &ind, const ir::Operand &obj) {
+    if (obj.isConstant())
+    {
+      VERBOSE(INTERPRETER) << "Allocate and assign constant tensor. operand index:" << ind.value()
+                           << std::endl;
+
+      auto const_tensor = std::make_shared<ROTensor>(obj.info());
+      // Assume that interpreter's tensor layout is same with model (NHWC)
+      const_tensor->setData(
+          std::make_shared<ir::ExternalData>(obj.data().base(), obj.info().total_size()));
+      interp_env->assignTensor(ind, const_tensor);
+    }
+  });
+
+  /*****************************************************************************
+   * Invoke interpreter
+   ****************************************************************************/
+
+  Interpreter interp(std::move(interp_env));
+  interp.run();
+
+  /*****************************************************************************
+   * Invoked interpreter run is finished
+   ****************************************************************************/
+
+  // If interpreter execute submodel
+  //  1. Get tensor output of submodel into tensor_map to save result
+  //  2. Generate new ExecEnv for next interpretation
+}
+
+} // namespace interp
+} // namespace exec
+} // namespace neurun